ABSTRACT

Historical CORONA imagery, a very high-resolution panchromatic imagery captured during 1960-72, has a lot of unexplored potentials to represent the land cover of the pre-Landsat era. This pre-Landsat land cover can be a valuable input to many long-term environmental modelling problems. While automated binary change detection and multi-class classification by manual photo-interpretation have been the two most popular applications of CORONA imagery, automated multi-class classification of CORONA imagery has not been explored. In this research, we propose two supervised classification methods capable of extracting spatial features for the classification of CORONA imagery. The first method utilizes two-dimensional convolutional neural networks (2D-CNN) for classification, whereas the second method involves stacking the CORONA imagery with its texture features obtained through geometric moments (GM) and performing pixel-wise classification using the random forest (RF) classifier and is termed as GM-RF. The proposed methods are tested on two study sites – the critical zone observatory (CZO) managed by the Indian Institute of Technology Kanpur in Uttar Pradesh (India) and a site near Chhapra in Bihar (India). For estimating the capabilities of the proposed methods and to compare the proposed methods with other methods, overall accuracy and Cohen’s kappa coefficient (k) along with z-scores statistics are used. The effects of various hyperparameters (e.g. percentage of training data, patch size, order of geometric moments) on accuracy are also evaluated. The classification accuracies achieved through the 2D-CNN and GM-RF method in terms of ĸ are higher than 90% for both study sites indicating that the proposed methods can classify CORONA imagery with little domain knowledge about the study areas, and hence can be used in a variety of applications.

摘要

历史的CORONA影像是在1960-72年间拍摄的非常高分辨率的全色影像，具有很多未开发的潜力，可以代表Landsat时代之前的土地覆被。Landsat之前的土地覆盖可能是许多长期环境建模问题的宝贵输入。尽管自动二进制变化检测和通过手动照片解释进行的多类别分类是CORONA图像的两个最流行的应用程序，但尚未探索CORONA图像的自动多类别分类。在这项研究中，我们提出了两种能够提取空间特征以进行CORONA图像分类的监督分类方法。第一种方法利用二维卷积神经网络（2D-CNN）进行分类，而第二种方法涉及将具有几何特征（GM）获得的纹理特征的CORONA影像堆叠在一起，并使用随机森林（RF）分类器执行逐像素分类，称为GM-RF。在两个研究地点（由印度北方邦的印度理工学院坎普尔分校管理的临界区天文台（CZO）和位于印度比哈尔邦Chhapra附近的一个地点）对这两个研究地点进行了测试。为了评估建议的方法的功能并将建议的方法与其他方法进行比较，整体准确性和Cohen的kappa系数（在两个研究地点（由印度北方邦的印度理工学院坎普尔分校管理的临界区天文台（CZO）和位于印度比哈尔邦Chhapra附近的一个地点）对这两个研究地点进行了测试。为了评估建议的方法的功能并将建议的方法与其他方法进行比较，整体准确性和Cohen的kappa系数（在两个研究地点（由印度北方邦的印度理工学院坎普尔分校管理的临界区天文台（CZO）和位于印度比哈尔邦Chhapra附近的一个地点）对这两个研究地点进行了测试。为了评估建议的方法的功能并将建议的方法与其他方法进行比较，整体准确性和Cohen的kappa系数（k）以及z -scores统计信息一起使用。还评估了各种超参数（例如，训练数据的百分比，补丁大小，几何矩的顺序）对准确性的影响。通过2D-CNN和GM-RF方法获得的分类精度在两个研究地点的ĸ均高于90％，这表明所提出的方法可以对CORONA影像进行分类，而对研究领域的了解很少，因此可以使用在各种应用中。